In this paper, we propose a new learning objective for GFlowNets, called subtrajectory balance (SubTB, or SubTB(\(\lambda \) ) when its real-valued hyperparameter \(\lambda \)  is specified). Building upon theoretical results of [1]}, [2]}, we show how the SubTB(\(\lambda \) ) objective allows the flexibility of learning from partial experiences of any length. Experiments on two synthetic and four real-world domains support the following empirical claims:
